A natural mutation between SARS-CoV-2 and SARS-CoV determines neutralization by a cross-reactive antibody

This article has been Reviewed by the following groups

Read the full article See related articles

Abstract

Epitopes that are conserved among SARS-like coronaviruses are attractive targets for design of cross-reactive vaccines and therapeutics. CR3022 is a SARS-CoV neutralizing antibody to a highly conserved epitope on the receptor binding domain (RBD) on the spike protein that is able to cross-react with SARS-CoV-2, but with lower affinity. Using x-ray crystallography, mutagenesis, and binding experiments, we illustrate that of four amino acid differences in the CR3022 epitope between SARS-CoV-2 and SARS-CoV, a single mutation P384A fully determines the affinity difference. CR3022 does not neutralize SARS-CoV-2, but the increased affinity to SARS-CoV-2 P384A mutant now enables neutralization with a similar potency to SARS-CoV. We further investigated CR3022 interaction with the SARS-CoV spike protein by negative-stain EM and cryo-EM. Three CR3022 Fabs bind per trimer with the RBD observed in different up-conformations due to considerable flexibility of the RBD. In one of these conformations, quaternary interactions are made by CR3022 to the N-terminal domain (NTD) of an adjacent subunit. Overall, this study provides insights into antigenic variation and potential cross-neutralizing epitopes on SARS-like viruses.

Article activity feed

  1. SciScore for 10.1101/2020.09.21.305441: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Experimental Models: Cell Lines
    SentencesResources
    Briefly, MLV-gag/pol and MLV-CMV plasmids was co-transfected into HEK293T cells along with full-length or P384A SARS-CoV-2 spike plasmids using Lipofectamine 2000 to produce pseudoviruses competent for single-round infection.
    HEK293T
    suggested: None
    At 42 to 48 hours post-infection, HeLa-hACE2 cells were lysed using 1x luciferase lysis buffer (25 mM Gly-Gly pH 7.8, 15 mM MgSO4, 4 mM EGTA, and 1% Triton X-100).
    HeLa-hACE2
    suggested: None
    Software and Algorithms
    SentencesResources
    Structures were solved by molecular replacement using PHASER [43] with PDB 6W41 for CR3022 Fab [20] and PDB 2AJF for SARS-CoV RBD [44].
    PHASER
    suggested: (Phaser, RRID:SCR_014219)
    Iterative model building and refinement were carried out in COOT [45] and PHENIX [46], respectively.
    COOT
    suggested: (Coot, RRID:SCR_014222)
    PHENIX
    suggested: (Phenix, RRID:SCR_014224)
    Ramachandran statistics were calculated using MolProbity [47].
    MolProbity
    suggested: (MolProbity, RRID:SCR_014226)
    Micrographs were collected using Leginon [48] and images were transferred to Appion [49] for particle picking using a difference-of-Gaussians picker (DoG-picker) [50] and generation of particle stacks.
    Leginon
    suggested: (Leginon, RRID:SCR_016731)
    Select 3D classes were auto-refined on Relion and used for making figures using UCSF Chimera [52].
    Relion
    suggested: (RELION, RRID:SCR_016274)
    Micrographs were collected through Leginon software at a nominal defocus range of -0.4 µm to -1.6 µm and MotionCor2 was used for alignment and dose weighting of the frames [48, 53].
    MotionCor2
    suggested: (MotionCor2, RRID:SCR_016499)
    Micrographs were transferred to CryoSPARC 2.9 for further processing [54].
    CryoSPARC
    suggested: (cryoSPARC, RRID:SCR_016501)
    CTF estimations were performed using GCTF and micrographs were selected using the Curate Exposures tool in CryoSPARC based on their CTF resolution estimates (cutoff 5 Å) for downstream particle picking, extraction and iterative rounds of 2D classification and selection [55].
    GCTF
    suggested: (GCTF, RRID:SCR_016500)
    Calculation of rotation angles: Comparisons of subunit rotation angles among different structures were performed with a software “Superpose” in the CCP4 package [56, 57].
    CCP4
    suggested: (CCP4, RRID:SCR_007255)

    Results from OddPub: Thank you for sharing your data.


    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.


    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.


    Results from JetFighter: We did not find any issues relating to colormaps.


    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.